Overview

Dataset statistics

Number of variables28
Number of observations12680520
Missing cells3875130
Missing cells (%)1.1%
Duplicate rows911694
Duplicate rows (%)7.2%
Total size in memory3.9 GiB
Average record size in memory328.0 B

Variable types

Numeric5
Text2
Categorical21

Alerts

Dataset has 911694 (7.2%) duplicate rowsDuplicates
RefYear is highly overall correlated with BRI_i and 1 other fieldsHigh correlation
v is highly overall correlated with gdp_iHigh correlation
gdp_i is highly overall correlated with vHigh correlation
BRI_i is highly overall correlated with RefYearHigh correlation
BRI_j is highly overall correlated with RefYearHigh correlation
rta is highly overall correlated with fta and 2 other fieldsHigh correlation
cu is highly overall correlated with cuandeiaHigh correlation
fta is highly overall correlated with rta and 1 other fieldsHigh correlation
eia is highly overall correlated with rta and 2 other fieldsHigh correlation
ps is highly overall correlated with rtaHigh correlation
cuandeia is highly overall correlated with cu and 1 other fieldsHigh correlation
ftaandeia is highly overall correlated with fta and 1 other fieldsHigh correlation
comlang_off is highly overall correlated with comlang_ethnoHigh correlation
comlang_ethno is highly overall correlated with comlang_offHigh correlation
BRICS_i is highly imbalanced (79.8%)Imbalance
BRICS_j is highly imbalanced (80.9%)Imbalance
BRI_OECD is highly imbalanced (99.9%)Imbalance
cu is highly imbalanced (68.8%)Imbalance
eia is highly imbalanced (63.4%)Imbalance
ps is highly imbalanced (61.8%)Imbalance
cuandeia is highly imbalanced (80.8%)Imbalance
ftaandeia is highly imbalanced (75.4%)Imbalance
contig is highly imbalanced (83.3%)Imbalance
colony is highly imbalanced (84.9%)Imbalance
comcol is highly imbalanced (57.6%)Imbalance
dist has 366468 (2.9%) missing valuesMissing
gdp_i has 649658 (5.1%) missing valuesMissing
gdp_j has 726811 (5.7%) missing valuesMissing
contig has 374472 (3.0%) missing valuesMissing
comlang_off has 374472 (3.0%) missing valuesMissing
comlang_ethno has 374472 (3.0%) missing valuesMissing
colony has 374472 (3.0%) missing valuesMissing
comcol has 374472 (3.0%) missing valuesMissing
v is highly skewed (γ1 = 79.0068557)Skewed

Reproduction

Analysis started2023-06-13 18:54:55.075141
Analysis finished2023-06-13 19:11:53.529020
Duration16 minutes and 58.45 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

RefYear
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.1088
Minimum1995
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.7 MiB
2023-06-13T21:11:53.658396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile1996
Q12003
median2011
Q32014
95-th percentile2020
Maximum2022
Range27
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.2587738
Coefficient of variation (CV)0.0036129321
Kurtosis-0.94700477
Mean2009.1088
Median Absolute Deviation (MAD)5
Skewness-0.28946508
Sum2.5476545 × 1010
Variance52.689796
MonotonicityNot monotonic
2023-06-13T21:11:53.824931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2013 1544487
 
12.2%
2014 1542834
 
12.2%
2007 416099
 
3.3%
2010 415668
 
3.3%
2008 415441
 
3.3%
2009 413973
 
3.3%
2006 412027
 
3.2%
2011 411256
 
3.2%
2005 409870
 
3.2%
2004 405026
 
3.2%
Other values (18) 6293839
49.6%
ValueCountFrequency (%)
1995 322706
2.5%
1996 342691
2.7%
1997 357139
2.8%
1998 361875
2.9%
1999 368373
2.9%
2000 383942
3.0%
2001 389185
3.1%
2002 395084
3.1%
2003 399749
3.2%
2004 405026
3.2%
ValueCountFrequency (%)
2022 218945
 
1.7%
2021 312700
 
2.5%
2020 323594
 
2.6%
2019 334403
 
2.6%
2018 342306
 
2.7%
2017 346452
 
2.7%
2016 344944
 
2.7%
2015 345155
 
2.7%
2014 1542834
12.2%
2013 1544487
12.2%

j
Text

Distinct227
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size725.6 MiB
2023-06-13T21:11:54.345468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters38041560
Distinct characters28
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowABW
2nd rowABW
3rd rowABW
4th rowABW
5th rowABW
ValueCountFrequency (%)
usa 137561
 
1.1%
fra 136716
 
1.1%
deu 136300
 
1.1%
nld 132737
 
1.0%
gbr 132551
 
1.0%
jpn 132371
 
1.0%
ita 132262
 
1.0%
esp 130412
 
1.0%
che 127452
 
1.0%
can 126740
 
1.0%
Other values (217) 11355418
89.6%
2023-06-13T21:11:54.989360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 3216464
 
8.5%
A 3167643
 
8.3%
N 2886819
 
7.6%
S 2034534
 
5.3%
L 1974161
 
5.2%
M 1956479
 
5.1%
G 1875467
 
4.9%
U 1846559
 
4.9%
T 1840837
 
4.8%
E 1804097
 
4.7%
Other values (18) 15438500
40.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38038486
> 99.9%
Decimal Number 3074
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 3216464
 
8.5%
A 3167643
 
8.3%
N 2886819
 
7.6%
S 2034534
 
5.3%
L 1974161
 
5.2%
M 1956479
 
5.1%
G 1875467
 
4.9%
U 1846559
 
4.9%
T 1840837
 
4.8%
E 1804097
 
4.7%
Other values (16) 15435426
40.6%
Decimal Number
ValueCountFrequency (%)
1 1537
50.0%
9 1537
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38038486
> 99.9%
Common 3074
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 3216464
 
8.5%
A 3167643
 
8.3%
N 2886819
 
7.6%
S 2034534
 
5.3%
L 1974161
 
5.2%
M 1956479
 
5.1%
G 1875467
 
4.9%
U 1846559
 
4.9%
T 1840837
 
4.8%
E 1804097
 
4.7%
Other values (16) 15435426
40.6%
Common
ValueCountFrequency (%)
1 1537
50.0%
9 1537
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38041560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 3216464
 
8.5%
A 3167643
 
8.3%
N 2886819
 
7.6%
S 2034534
 
5.3%
L 1974161
 
5.2%
M 1956479
 
5.1%
G 1875467
 
4.9%
U 1846559
 
4.9%
T 1840837
 
4.8%
E 1804097
 
4.7%
Other values (18) 15438500
40.6%

i
Text

Distinct251
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size725.6 MiB
2023-06-13T21:11:55.441733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters38041560
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAIA
2nd rowALB
3rd rowAND
4th rowANT
5th rowANT
ValueCountFrequency (%)
usa 127932
 
1.0%
fra 127323
 
1.0%
gbr 126272
 
1.0%
deu 126096
 
1.0%
nld 124015
 
1.0%
jpn 123471
 
1.0%
ita 123198
 
1.0%
esp 121303
 
1.0%
can 120977
 
1.0%
kor 120552
 
1.0%
Other values (241) 11439381
90.2%
2023-06-13T21:11:56.027400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 3178044
 
8.4%
A 3135459
 
8.2%
N 2884309
 
7.6%
M 2030662
 
5.3%
S 2014459
 
5.3%
L 1962871
 
5.2%
G 1926838
 
5.1%
T 1836693
 
4.8%
U 1806459
 
4.7%
B 1775049
 
4.7%
Other values (24) 15490717
40.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38033028
> 99.9%
Decimal Number 6302
 
< 0.1%
Space Separator 1345
 
< 0.1%
Connector Punctuation 885
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 3178044
 
8.4%
A 3135459
 
8.2%
N 2884309
 
7.6%
M 2030662
 
5.3%
S 2014459
 
5.3%
L 1962871
 
5.2%
G 1926838
 
5.1%
T 1836693
 
4.8%
U 1806459
 
4.7%
B 1775049
 
4.7%
Other values (16) 15482185
40.7%
Decimal Number
ValueCountFrequency (%)
0 2486
39.4%
1 1940
30.8%
9 1731
27.5%
2 86
 
1.4%
5 35
 
0.6%
7 24
 
0.4%
Space Separator
ValueCountFrequency (%)
1345
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 885
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38033028
> 99.9%
Common 8532
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 3178044
 
8.4%
A 3135459
 
8.2%
N 2884309
 
7.6%
M 2030662
 
5.3%
S 2014459
 
5.3%
L 1962871
 
5.2%
G 1926838
 
5.1%
T 1836693
 
4.8%
U 1806459
 
4.7%
B 1775049
 
4.7%
Other values (16) 15482185
40.7%
Common
ValueCountFrequency (%)
0 2486
29.1%
1 1940
22.7%
9 1731
20.3%
1345
15.8%
_ 885
 
10.4%
2 86
 
1.0%
5 35
 
0.4%
7 24
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38041560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 3178044
 
8.4%
A 3135459
 
8.2%
N 2884309
 
7.6%
M 2030662
 
5.3%
S 2014459
 
5.3%
L 1962871
 
5.2%
G 1926838
 
5.1%
T 1836693
 
4.8%
U 1806459
 
4.7%
B 1775049
 
4.7%
Other values (24) 15490717
40.7%

v
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct623050
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9292442 × 108
Minimum0.005
Maximum3.5936014 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.7 MiB
2023-06-13T21:11:56.233055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile9516
Q1412040.04
median6791335
Q379972808
95-th percentile1.9672031 × 109
Maximum3.5936014 × 1012
Range3.5936014 × 1012
Interquartile range (IQR)79560768

Descriptive statistics

Standard deviation7.040165 × 109
Coefficient of variation (CV)10.160076
Kurtosis20289.271
Mean6.9292442 × 108
Median Absolute Deviation (MAD)6773368.2
Skewness79.006856
Sum8.7866419 × 1015
Variance4.9563923 × 1019
MonotonicityNot monotonic
2023-06-13T21:11:56.405026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 4583
 
< 0.1%
2000 2566
 
< 0.1%
3000 1968
 
< 0.1%
4000 1629
 
< 0.1%
5000 1623
 
< 0.1%
6000 1196
 
< 0.1%
1 1142
 
< 0.1%
10000 1000
 
< 0.1%
8000 935
 
< 0.1%
7000 833
 
< 0.1%
Other values (623040) 12663045
99.9%
ValueCountFrequency (%)
0.005 21
< 0.1%
0.006 4
 
< 0.1%
0.009 15
< 0.1%
0.012 1
 
< 0.1%
0.059 19
< 0.1%
0.072 20
< 0.1%
0.1 7
 
< 0.1%
0.103 1
 
< 0.1%
0.138 1
 
< 0.1%
0.14 10
< 0.1%
ValueCountFrequency (%)
3.593601436 × 10121
< 0.1%
3.362301613 × 10121
< 0.1%
2.589098353 × 10121
< 0.1%
2.499206994 × 10121
< 0.1%
2.48643972 × 10121
< 0.1%
2.273468224 × 10121
< 0.1%
2.263370504 × 10121
< 0.1%
2.097637172 × 10121
< 0.1%
2.062089833 × 10121
< 0.1%
1.753136708 × 10121
< 0.1%

dist
Real number (ℝ)

Distinct19092
Distinct (%)0.2%
Missing366468
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean7179.519
Minimum10.47888
Maximum19951.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.7 MiB
2023-06-13T21:11:56.597905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.47888
5-th percentile1051.403
Q13550.955
median6679.382
Q310039.22
95-th percentile15811.19
Maximum19951.16
Range19940.681
Interquartile range (IQR)6488.265

Descriptive statistics

Standard deviation4470.0863
Coefficient of variation (CV)0.6226164
Kurtosis-0.43989836
Mean7179.519
Median Absolute Deviation (MAD)3245.586
Skewness0.53231861
Sum8.840897 × 1010
Variance19981671
MonotonicityNot monotonic
2023-06-13T21:11:56.808845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12522 2646
 
< 0.1%
16180.32 2586
 
< 0.1%
6562.448 2580
 
< 0.1%
2628.122 2566
 
< 0.1%
11304.19 2189
 
< 0.1%
3195.369 2082
 
< 0.1%
13836.31 1926
 
< 0.1%
10424.03 1704
 
< 0.1%
10529 1651
 
< 0.1%
3503.608 1564
 
< 0.1%
Other values (19082) 12292558
96.9%
(Missing) 366468
 
2.9%
ValueCountFrequency (%)
10.47888 160
 
< 0.1%
59.61723 1360
< 0.1%
60.77057 1360
< 0.1%
63.18423 752
< 0.1%
80.98481 1360
< 0.1%
83.94967 969
< 0.1%
85.94135 990
< 0.1%
94.83208 1083
< 0.1%
95.38572 1102
< 0.1%
105.1806 1216
< 0.1%
ValueCountFrequency (%)
19951.16 838
< 0.1%
19904.45 8
 
< 0.1%
19888.66 4
 
< 0.1%
19846.2 2
 
< 0.1%
19812.04 658
< 0.1%
19772.34 1320
< 0.1%
19747.4 1260
< 0.1%
19737.02 7
 
< 0.1%
19719.34 24
 
< 0.1%
19712.39 1
 
< 0.1%

gdp_i
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5190
Distinct (%)< 0.1%
Missing649658
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean6.0740455 × 1011
Minimum11025945
Maximum2.3315081 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.7 MiB
2023-06-13T21:11:57.684600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11025945
5-th percentile1.3861886 × 109
Q11.3495063 × 1010
median7.3141 × 1010
Q33.5612822 × 1011
95-th percentile2.6162 × 1012
Maximum2.3315081 × 1013
Range2.331507 × 1013
Interquartile range (IQR)3.4263316 × 1011

Descriptive statistics

Standard deviation1.9418239 × 1012
Coefficient of variation (CV)3.1969203
Kurtosis53.514669
Mean6.0740455 × 1011
Median Absolute Deviation (MAD)7.0128978 × 1010
Skewness6.7587231
Sum7.3076003 × 1018
Variance3.7706802 × 1024
MonotonicityNot monotonic
2023-06-13T21:11:57.839385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.90521 × 10119629
 
0.1%
2.855964489 × 10127783
 
0.1%
2.84931 × 10127783
 
0.1%
1.73931 × 10137748
 
0.1%
1.755068017 × 10137748
 
0.1%
3.889093051 × 10127735
 
0.1%
3.87928 × 10127735
 
0.1%
1.684319099 × 10137720
 
0.1%
1.66915 × 10137720
 
0.1%
2.99883 × 10127711
 
0.1%
Other values (5180) 11951550
94.3%
(Missing) 649658
 
5.1%
ValueCountFrequency (%)
11025945.14 107
 
< 0.1%
12334846.23 119
 
< 0.1%
12700905.45 126
 
< 0.1%
12757632.87 125
 
< 0.1%
13196544.95 171
< 0.1%
13687141.11 167
< 0.1%
13742057.05 183
< 0.1%
15450994.24 249
< 0.1%
18231078.54 231
< 0.1%
20432742.11 344
< 0.1%
ValueCountFrequency (%)
2.331508056 × 10134068
< 0.1%
2.138097612 × 10134069
< 0.1%
2.106047361 × 10134053
< 0.1%
2.053305731 × 10134077
< 0.1%
1.947733655 × 10134077
< 0.1%
1.869511084 × 10134078
< 0.1%
1.820602074 × 10134077
< 0.1%
1.773406265 × 10133596
< 0.1%
1.755068017 × 10137748
0.1%
1.73931 × 10137748
0.1%

gdp_j
Real number (ℝ)

Distinct5466
Distinct (%)< 0.1%
Missing726811
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean5.7734848 × 1011
Minimum11025945
Maximum2.3315081 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.7 MiB
2023-06-13T21:11:57.993071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11025945
5-th percentile1.2743303 × 109
Q11.268307 × 1010
median6.5292741 × 1010
Q33.3761968 × 1011
95-th percentile2.47281 × 1012
Maximum2.3315081 × 1013
Range2.331507 × 1013
Interquartile range (IQR)3.2493661 × 1011

Descriptive statistics

Standard deviation1.8627736 × 1012
Coefficient of variation (CV)3.2264285
Kurtosis54.975944
Mean5.7734848 × 1011
Median Absolute Deviation (MAD)6.2793808 × 1010
Skewness6.8341962
Sum6.9014557 × 1018
Variance3.4699254 × 1024
MonotonicityNot monotonic
2023-06-13T21:11:58.143971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.90521 × 10119897
 
0.1%
2.84931 × 10127845
 
0.1%
2.855964489 × 10127845
 
0.1%
2.811876903 × 10127825
 
0.1%
2.80851 × 10127825
 
0.1%
3.06522328 × 10127779
 
0.1%
2.99883 × 10127779
 
0.1%
1.66915 × 10137763
 
0.1%
1.684319099 × 10137763
 
0.1%
2.786315215 × 10127758
 
0.1%
Other values (5456) 11873630
93.6%
(Missing) 726811
 
5.7%
ValueCountFrequency (%)
11025945.14 151
 
< 0.1%
12334846.23 211
< 0.1%
12700905.45 224
< 0.1%
12757632.87 229
< 0.1%
13196544.95 214
< 0.1%
13687141.11 177
< 0.1%
13742057.05 190
< 0.1%
15450994.24 272
< 0.1%
18231078.54 249
< 0.1%
20432742.11 380
< 0.1%
ValueCountFrequency (%)
2.331508056 × 10132728
 
< 0.1%
2.138097612 × 10132963
 
< 0.1%
2.106047361 × 10132868
 
< 0.1%
2.053305731 × 10133097
< 0.1%
1.947733655 × 10133158
< 0.1%
1.869511084 × 10133137
< 0.1%
1.820602074 × 10133182
< 0.1%
1.773406265 × 10132647
 
< 0.1%
1.755068017 × 10137739
0.1%
1.73931 × 10137739
0.1%

BRI_i
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size701.4 MiB
0
11144641 
1
1535879 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12680520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11144641
87.9%
1 1535879
 
12.1%

Length

2023-06-13T21:11:58.291926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:11:58.432148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11144641
87.9%
1 1535879
 
12.1%

Most occurring characters

ValueCountFrequency (%)
0 11144641
87.9%
1 1535879
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12680520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11144641
87.9%
1 1535879
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12680520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11144641
87.9%
1 1535879
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12680520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11144641
87.9%
1 1535879
 
12.1%

BRI_j
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size701.4 MiB
0
11168030 
1
1512490 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12680520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11168030
88.1%
1 1512490
 
11.9%

Length

2023-06-13T21:11:58.535934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:11:58.667258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11168030
88.1%
1 1512490
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 11168030
88.1%
1 1512490
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12680520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11168030
88.1%
1 1512490
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 12680520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11168030
88.1%
1 1512490
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12680520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11168030
88.1%
1 1512490
 
11.9%

WTO_i
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size701.4 MiB
1
9921417 
0
2759103 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12680520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 9921417
78.2%
0 2759103
 
21.8%

Length

2023-06-13T21:11:58.776996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:11:58.902363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9921417
78.2%
0 2759103
 
21.8%

Most occurring characters

ValueCountFrequency (%)
1 9921417
78.2%
0 2759103
 
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12680520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9921417
78.2%
0 2759103
 
21.8%

Most occurring scripts

ValueCountFrequency (%)
Common 12680520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9921417
78.2%
0 2759103
 
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12680520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9921417
78.2%
0 2759103
 
21.8%

WTO_j
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size701.4 MiB
1
9759951 
0
2920569 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12680520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 9759951
77.0%
0 2920569
 
23.0%

Length

2023-06-13T21:11:59.004902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:11:59.126831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9759951
77.0%
0 2920569
 
23.0%

Most occurring characters

ValueCountFrequency (%)
1 9759951
77.0%
0 2920569
 
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12680520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9759951
77.0%
0 2920569
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12680520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9759951
77.0%
0 2920569
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12680520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9759951
77.0%
0 2920569
 
23.0%

OECD_i
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size701.4 MiB
0
9352858 
1
3327662 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12680520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9352858
73.8%
1 3327662
 
26.2%

Length

2023-06-13T21:11:59.238587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:11:59.370240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9352858
73.8%
1 3327662
 
26.2%

Most occurring characters

ValueCountFrequency (%)
0 9352858
73.8%
1 3327662
 
26.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12680520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9352858
73.8%
1 3327662
 
26.2%

Most occurring scripts

ValueCountFrequency (%)
Common 12680520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9352858
73.8%
1 3327662
 
26.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12680520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9352858
73.8%
1 3327662
 
26.2%

OECD_j
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size701.4 MiB
0
9563024 
1
3117496 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12680520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9563024
75.4%
1 3117496
 
24.6%

Length

2023-06-13T21:11:59.472301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:11:59.611750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9563024
75.4%
1 3117496
 
24.6%

Most occurring characters

ValueCountFrequency (%)
0 9563024
75.4%
1 3117496
 
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12680520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9563024
75.4%
1 3117496
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common 12680520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9563024
75.4%
1 3117496
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12680520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9563024
75.4%
1 3117496
 
24.6%

BRICS_i
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size701.4 MiB
0
12279920 
1
 
400600

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12680520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12279920
96.8%
1 400600
 
3.2%

Length

2023-06-13T21:11:59.724037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:11:59.854888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12279920
96.8%
1 400600
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 12279920
96.8%
1 400600
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12680520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12279920
96.8%
1 400600
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 12680520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12279920
96.8%
1 400600
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12680520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12279920
96.8%
1 400600
 
3.2%

BRICS_j
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size701.4 MiB
0
12307989 
1
 
372531

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12680520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12307989
97.1%
1 372531
 
2.9%

Length

2023-06-13T21:11:59.980703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:00.128825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12307989
97.1%
1 372531
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 12307989
97.1%
1 372531
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12680520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12307989
97.1%
1 372531
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 12680520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12307989
97.1%
1 372531
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12680520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12307989
97.1%
1 372531
 
2.9%

BRI_OECD
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size701.4 MiB
0
12679280 
1
 
1240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12680520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12679280
> 99.9%
1 1240
 
< 0.1%

Length

2023-06-13T21:12:00.241630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:00.383713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12679280
> 99.9%
1 1240
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12679280
> 99.9%
1 1240
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12680520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12679280
> 99.9%
1 1240
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12680520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12679280
> 99.9%
1 1240
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12680520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12679280
> 99.9%
1 1240
 
< 0.1%

rta
Categorical

Distinct2
Distinct (%)< 0.1%
Missing37119
Missing (%)0.3%
Memory size724.9 MiB
0.0
9746052 
1.0
2897349 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37930203
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 9746052
76.9%
1.0 2897349
 
22.8%
(Missing) 37119
 
0.3%

Length

2023-06-13T21:12:00.494939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:00.638600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9746052
77.1%
1.0 2897349
 
22.9%

Most occurring characters

ValueCountFrequency (%)
0 22389453
59.0%
. 12643401
33.3%
1 2897349
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25286802
66.7%
Other Punctuation 12643401
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22389453
88.5%
1 2897349
 
11.5%
Other Punctuation
ValueCountFrequency (%)
. 12643401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37930203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22389453
59.0%
. 12643401
33.3%
1 2897349
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37930203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22389453
59.0%
. 12643401
33.3%
1 2897349
 
7.6%

cu
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing37119
Missing (%)0.3%
Memory size724.9 MiB
0.0
11934685 
1.0
 
708716

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37930203
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 11934685
94.1%
1.0 708716
 
5.6%
(Missing) 37119
 
0.3%

Length

2023-06-13T21:12:00.756228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:00.900604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11934685
94.4%
1.0 708716
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 24578086
64.8%
. 12643401
33.3%
1 708716
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25286802
66.7%
Other Punctuation 12643401
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24578086
97.2%
1 708716
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 12643401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37930203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24578086
64.8%
. 12643401
33.3%
1 708716
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37930203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24578086
64.8%
. 12643401
33.3%
1 708716
 
1.9%

fta
Categorical

Distinct2
Distinct (%)< 0.1%
Missing37119
Missing (%)0.3%
Memory size724.9 MiB
0.0
11222258 
1.0
1421143 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37930203
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11222258
88.5%
1.0 1421143
 
11.2%
(Missing) 37119
 
0.3%

Length

2023-06-13T21:12:01.018865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:01.154006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11222258
88.8%
1.0 1421143
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 23865659
62.9%
. 12643401
33.3%
1 1421143
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25286802
66.7%
Other Punctuation 12643401
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23865659
94.4%
1 1421143
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 12643401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37930203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23865659
62.9%
. 12643401
33.3%
1 1421143
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37930203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23865659
62.9%
. 12643401
33.3%
1 1421143
 
3.7%

eia
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing37119
Missing (%)0.3%
Memory size724.9 MiB
0.0
11759063 
1.0
 
884338

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37930203
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 11759063
92.7%
1.0 884338
 
7.0%
(Missing) 37119
 
0.3%

Length

2023-06-13T21:12:01.263206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:01.402117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11759063
93.0%
1.0 884338
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 24402464
64.3%
. 12643401
33.3%
1 884338
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25286802
66.7%
Other Punctuation 12643401
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24402464
96.5%
1 884338
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 12643401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37930203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24402464
64.3%
. 12643401
33.3%
1 884338
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37930203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24402464
64.3%
. 12643401
33.3%
1 884338
 
2.3%

ps
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing37119
Missing (%)0.3%
Memory size724.9 MiB
0.0
11702276 
1.0
 
941125

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37930203
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11702276
92.3%
1.0 941125
 
7.4%
(Missing) 37119
 
0.3%

Length

2023-06-13T21:12:01.521185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:01.652667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11702276
92.6%
1.0 941125
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 24345677
64.2%
. 12643401
33.3%
1 941125
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25286802
66.7%
Other Punctuation 12643401
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24345677
96.3%
1 941125
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 12643401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37930203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24345677
64.2%
. 12643401
33.3%
1 941125
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37930203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24345677
64.2%
. 12643401
33.3%
1 941125
 
2.5%

cuandeia
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing37119
Missing (%)0.3%
Memory size724.9 MiB
0.0
12270969 
1.0
 
372432

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37930203
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 12270969
96.8%
1.0 372432
 
2.9%
(Missing) 37119
 
0.3%

Length

2023-06-13T21:12:01.762894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:01.895596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 12270969
97.1%
1.0 372432
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 24914370
65.7%
. 12643401
33.3%
1 372432
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25286802
66.7%
Other Punctuation 12643401
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24914370
98.5%
1 372432
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 12643401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37930203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24914370
65.7%
. 12643401
33.3%
1 372432
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37930203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24914370
65.7%
. 12643401
33.3%
1 372432
 
1.0%

ftaandeia
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing37119
Missing (%)0.3%
Memory size724.9 MiB
0.0
12126883 
1.0
 
516518

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37930203
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 12126883
95.6%
1.0 516518
 
4.1%
(Missing) 37119
 
0.3%

Length

2023-06-13T21:12:02.006607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:02.138624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 12126883
95.9%
1.0 516518
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 24770284
65.3%
. 12643401
33.3%
1 516518
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25286802
66.7%
Other Punctuation 12643401
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24770284
98.0%
1 516518
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 12643401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37930203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24770284
65.3%
. 12643401
33.3%
1 516518
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37930203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24770284
65.3%
. 12643401
33.3%
1 516518
 
1.4%

contig
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing374472
Missing (%)3.0%
Memory size718.4 MiB
0.0
12001216 
1.0
 
304832

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36918144
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 12001216
94.6%
1.0 304832
 
2.4%
(Missing) 374472
 
3.0%

Length

2023-06-13T21:12:02.251933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:02.381176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 12001216
97.5%
1.0 304832
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 24307264
65.8%
. 12306048
33.3%
1 304832
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24612096
66.7%
Other Punctuation 12306048
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24307264
98.8%
1 304832
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 12306048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36918144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24307264
65.8%
. 12306048
33.3%
1 304832
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36918144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24307264
65.8%
. 12306048
33.3%
1 304832
 
0.8%

comlang_off
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing374472
Missing (%)3.0%
Memory size718.4 MiB
0.0
10366406 
1.0
1939642 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36918144
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 10366406
81.8%
1.0 1939642
 
15.3%
(Missing) 374472
 
3.0%

Length

2023-06-13T21:12:02.491511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:02.616042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10366406
84.2%
1.0 1939642
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 22672454
61.4%
. 12306048
33.3%
1 1939642
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24612096
66.7%
Other Punctuation 12306048
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22672454
92.1%
1 1939642
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 12306048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36918144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22672454
61.4%
. 12306048
33.3%
1 1939642
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36918144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22672454
61.4%
. 12306048
33.3%
1 1939642
 
5.3%

comlang_ethno
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing374472
Missing (%)3.0%
Memory size718.4 MiB
0.0
10294620 
1.0
2011428 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36918144
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 10294620
81.2%
1.0 2011428
 
15.9%
(Missing) 374472
 
3.0%

Length

2023-06-13T21:12:02.737231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:02.871521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10294620
83.7%
1.0 2011428
 
16.3%

Most occurring characters

ValueCountFrequency (%)
0 22600668
61.2%
. 12306048
33.3%
1 2011428
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24612096
66.7%
Other Punctuation 12306048
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22600668
91.8%
1 2011428
 
8.2%
Other Punctuation
ValueCountFrequency (%)
. 12306048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36918144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22600668
61.2%
. 12306048
33.3%
1 2011428
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36918144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22600668
61.2%
. 12306048
33.3%
1 2011428
 
5.4%

colony
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing374472
Missing (%)3.0%
Memory size718.4 MiB
0.0
12037879 
1.0
 
268169

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36918144
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 12037879
94.9%
1.0 268169
 
2.1%
(Missing) 374472
 
3.0%

Length

2023-06-13T21:12:02.997204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:03.136775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 12037879
97.8%
1.0 268169
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 24343927
65.9%
. 12306048
33.3%
1 268169
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24612096
66.7%
Other Punctuation 12306048
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24343927
98.9%
1 268169
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 12306048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36918144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24343927
65.9%
. 12306048
33.3%
1 268169
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36918144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24343927
65.9%
. 12306048
33.3%
1 268169
 
0.7%

comcol
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing374472
Missing (%)3.0%
Memory size718.4 MiB
0.0
11244233 
1.0
 
1061815

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36918144
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 11244233
88.7%
1.0 1061815
 
8.4%
(Missing) 374472
 
3.0%

Length

2023-06-13T21:12:03.282722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T21:12:03.428947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11244233
91.4%
1.0 1061815
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 23550281
63.8%
. 12306048
33.3%
1 1061815
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24612096
66.7%
Other Punctuation 12306048
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23550281
95.7%
1 1061815
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 12306048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36918144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23550281
63.8%
. 12306048
33.3%
1 1061815
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36918144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23550281
63.8%
. 12306048
33.3%
1 1061815
 
2.9%

Interactions

2023-06-13T21:08:17.216434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:30.376549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:43.515160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:55.294516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:08:04.375394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:08:19.759080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:33.055698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:45.341376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:57.195309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:08:06.028895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:08:21.781512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:35.704117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:47.324039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:58.769843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:08:08.050026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:08:23.773335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:38.267831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:49.371041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:08:00.651879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:08:10.241031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:08:25.810477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:40.702353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:07:51.229036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:08:02.342062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T21:08:12.664863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-13T21:12:03.596536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
RefYearvdistgdp_igdp_jBRI_iBRI_jWTO_iWTO_jOECD_iOECD_jBRICS_iBRICS_jBRI_OECDrtacuftaeiapscuandeiaftaandeiacontigcomlang_offcomlang_ethnocolonycomcol
RefYear1.0000.1160.0020.2090.1660.6260.6290.1280.0900.0870.0250.1570.1500.0130.0140.0080.0120.0170.0100.0110.0140.0060.0130.0110.0070.012
v0.1161.000-0.1690.5280.3120.0110.0020.0030.0030.0010.0040.0280.0000.0000.0010.0010.0010.0020.0020.0010.0040.0060.0010.0000.0010.002
dist0.002-0.1691.0000.1020.0940.0390.0380.1040.1030.0640.0650.0700.0680.0150.4010.4510.2710.3250.1240.3410.1530.3400.1520.1360.0450.162
gdp_i0.2090.5280.1021.000-0.2180.1390.0660.1200.0520.2830.0530.2860.0130.0360.0720.0440.0470.0500.0500.0520.0230.0200.0560.0760.1530.070
gdp_j0.1660.3120.094-0.2181.0000.0600.1390.0490.1190.0530.2810.0130.2950.0360.0700.0450.0440.0530.0490.0550.0240.0190.0530.0730.1430.069
BRI_i0.6260.0110.0390.1390.0601.0000.4240.0230.0330.0350.0110.1180.0460.0270.0060.0040.0050.0100.0040.0100.0060.0180.0470.0460.0170.001
BRI_j0.6290.0020.0380.0660.1390.4241.0000.0670.0200.0530.0680.0630.1050.0270.0080.0040.0100.0100.0080.0090.0070.0170.0400.0430.0120.000
WTO_i0.1280.0030.1040.1200.0490.0230.0671.0000.0420.2270.0880.0850.0090.0050.0300.0310.0150.0860.0340.0540.0650.0190.0140.0270.0040.009
WTO_j0.0900.0030.1030.0520.1190.0330.0200.0421.0000.0920.2290.0120.0840.0050.0270.0350.0220.0920.0340.0580.0700.0160.0100.0220.0080.010
OECD_i0.0870.0010.0640.2830.0530.0350.0530.2270.0921.0000.1170.0500.0280.0060.0570.0600.1170.1730.1230.1250.1220.0250.0960.1030.0800.173
OECD_j0.0250.0040.0650.0530.2810.0110.0680.0880.2290.1171.0000.0280.0400.0060.0620.0690.1140.1830.1190.1350.1270.0200.0930.0980.0750.165
BRICS_i0.1570.0280.0700.2860.0130.1180.0630.0850.0120.0500.0281.0000.0010.0550.0170.0390.0260.0260.0380.0300.0120.0280.0080.0080.0020.023
BRICS_j0.1500.0000.0680.0130.2950.0460.1050.0090.0840.0280.0400.0011.0000.0570.0160.0370.0220.0240.0350.0290.0100.0280.0110.0110.0000.022
BRI_OECD0.0130.0000.0150.0360.0360.0270.0270.0050.0050.0060.0060.0550.0571.0000.0050.0020.0040.0030.0030.0020.0020.0310.0040.0040.0010.003
rta0.0140.0010.4010.0720.0700.0060.0080.0300.0270.0570.0620.0170.0160.0051.0000.4470.6530.5030.5200.3200.3790.1960.1080.1100.0320.090
cu0.0080.0010.4510.0440.0450.0040.0040.0310.0350.0600.0690.0390.0370.0020.4471.0000.0290.4350.0200.7150.0200.2270.1220.1120.0120.141
fta0.0120.0010.2710.0470.0440.0050.0100.0150.0220.1170.1140.0260.0220.0040.6530.0291.0000.4100.0090.0360.5800.1060.0460.0570.0670.002
eia0.0170.0020.3250.0500.0530.0100.0100.0860.0920.1730.1830.0260.0240.0030.5030.4350.4101.0000.0400.6350.7530.0840.0030.0080.0090.004
ps0.0100.0020.1240.0500.0490.0040.0080.0340.0340.1230.1190.0380.0350.0030.5200.0200.0090.0401.0000.0390.0270.0890.0590.0620.0280.057
cuandeia0.0110.0010.3410.0520.0550.0100.0090.0540.0580.1250.1350.0300.0290.0020.3200.7150.0360.6350.0391.0000.0060.0950.0270.0250.0040.051
ftaandeia0.0140.0040.1530.0230.0240.0060.0070.0650.0700.1220.1270.0120.0100.0020.3790.0200.5800.7530.0270.0061.0000.0350.0230.0140.0090.049
contig0.0060.0060.3400.0200.0190.0180.0170.0190.0160.0250.0200.0280.0280.0310.1960.2270.1060.0840.0890.0950.0351.0000.1160.1110.0820.086
comlang_off0.0130.0010.1520.0560.0530.0470.0400.0140.0100.0960.0930.0080.0110.0040.1080.1220.0460.0030.0590.0270.0230.1161.0000.8270.1880.348
comlang_ethno0.0110.0000.1360.0760.0730.0460.0430.0270.0220.1030.0980.0080.0110.0040.1100.1120.0570.0080.0620.0250.0140.1110.8271.0000.1810.303
colony0.0070.0010.0450.1530.1430.0170.0120.0040.0080.0800.0750.0020.0000.0010.0320.0120.0670.0090.0280.0040.0090.0820.1880.1811.0000.046
comcol0.0120.0020.1620.0700.0690.0010.0000.0090.0100.1730.1650.0230.0220.0030.0900.1410.0020.0040.0570.0510.0490.0860.3480.3030.0461.000

Missing values

2023-06-13T21:08:28.844175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-13T21:09:32.174835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-13T21:11:27.335669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

RefYearjivdistgdp_igdp_jBRI_iBRI_jWTO_iWTO_jOECD_iOECD_jBRICS_iBRICS_jBRI_OECDrtacuftaeiapscuandeiaftaandeiacontigcomlang_offcomlang_ethnocolonycomcol
02008ABWAIA5.084000e+03983.26822.791961e+09NaN000000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11996ABWALB1.533525e+049091.74201.379888e+093.314898e+09000000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21997ABWAND1.174000e+037572.78801.531844e+091.180597e+090000000000.00.00.00.00.00.00.00.01.00.00.00.0
32005ABWANT2.638984e+07136.38482.331006e+09NaN0000000001.01.00.01.00.01.00.00.01.01.00.01.0
42005ABWANT2.638984e+07136.38482.331006e+09NaN0000000001.01.00.01.00.01.00.00.01.01.00.01.0
52005ABWANT2.638984e+07136.38482.331006e+09NaN0000000001.01.00.01.00.01.00.00.01.01.00.01.0
62005ABWANT2.638984e+07136.38482.331006e+09NaN0000000001.01.00.01.00.01.00.00.01.01.00.01.0
72005ABWANT2.638984e+07136.38482.331006e+09NaN0000000001.01.00.01.00.01.00.00.01.01.00.01.0
82005ABWANT2.638984e+07136.38482.331006e+09NaN0000000001.01.00.01.00.01.00.00.01.01.00.01.0
92006ABWANT2.400926e+07136.38482.421475e+09NaN0000000001.01.00.01.00.01.00.00.01.01.00.01.0
RefYearjivdistgdp_igdp_jBRI_iBRI_jWTO_iWTO_jOECD_iOECD_jBRICS_iBRICS_jBRI_OECDrtacuftaeiapscuandeiaftaandeiacontigcomlang_offcomlang_ethnocolonycomcol
126805102022UZBUSA1.655208e+0710179.660NaNNaN1001000000.00.00.00.00.00.00.00.00.00.00.00.0
126805112022UZBUSA1.655208e+0710179.660NaNNaN1001000000.00.00.00.00.00.00.00.00.00.00.00.0
126805122022UZBUSA1.655208e+0710179.660NaNNaN1001000000.00.00.00.00.00.00.00.00.00.00.00.0
126805132022UZBUSA1.655208e+0710179.660NaNNaN1001000000.00.00.00.00.00.00.00.00.00.00.00.0
126805142022UZBUSA1.655208e+0710179.660NaNNaN1001000000.00.00.00.00.00.00.00.00.00.00.00.0
126805152022UZBUSA1.655208e+0710179.660NaNNaN1001000000.00.00.00.00.00.00.00.00.00.00.00.0
126805162022UZBUSA1.655208e+0710179.660NaNNaN1001000000.00.00.00.00.00.00.00.00.00.00.00.0
126805172022UZBVEN4.476500e+0412722.760NaNNaN1101000000.00.00.00.00.00.00.00.00.00.00.00.0
126805182022UZBYEM4.488000e+043750.189NaNNaN110100000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
126805192022UZB_X4.575512e+09NaNNaNNaN100000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

RefYearjivdistgdp_igdp_jBRI_iBRI_jWTO_iWTO_jOECD_iOECD_jBRICS_iBRICS_jBRI_OECDrtacuftaeiapscuandeiaftaandeiacontigcomlang_offcomlang_ethnocolonycomcol# duplicates
8961995AUSSDN2.733538e+0613599.5503.683920e+111.382974e+100010100000.00.00.00.00.00.00.00.00.00.00.00.034
11301995AUTSDN1.068922e+073925.5172.404580e+111.382974e+100010100000.00.00.00.00.00.00.00.00.00.00.00.034
15041995BGDSDN1.240609e+076091.6853.793975e+101.382974e+100010000001.00.00.00.01.00.00.00.00.00.00.01.034
16681995BGRSDN8.118186e+063144.0551.306342e+101.382974e+100000000000.00.00.00.00.00.00.00.00.00.00.00.034
21821995BLXSDN4.111045e+074664.8153.111562e+111.382974e+100000000000.00.00.00.00.00.00.00.00.00.00.00.034
26111995BRASDN1.593438e+069640.1957.856430e+111.382974e+100010000001.00.00.00.01.00.00.00.00.00.00.00.034
30321995CANSDN2.786686e+0610496.3706.040320e+111.382974e+100010100000.00.00.00.00.00.00.00.00.00.00.00.034
32551995CHESDN2.832253e+074193.6373.417590e+111.382974e+100010100000.00.00.00.00.00.00.00.00.00.00.00.034
36901995CHNSDN4.794082e+078402.0477.345480e+111.382974e+100000000000.00.00.00.00.00.00.00.00.00.00.00.034
50711995CZESDN3.763960e+064176.8775.953711e+101.382974e+100010100000.00.00.00.00.00.00.00.00.00.00.00.034